Computer systems can utilize a wide array of sensors to measure their users' affective responses, such as the users' physiological and/or behavioral signals. For example, various types of sensors can measure physiological signals such as heart rate, blood-volume pulse, galvanic skin response (GSR), skin temperature, respiration, or brainwave activity such as electroencephalography (EEG). In addition, sensors such as cameras can be used to measure behavioral cues such as movement, gestures, and/or expression. Interpreting these signals enables the systems to determine the users' emotional responses. This paves the way to what is often called affective computing, which involves applications that can take into account a user's feelings in order to improve the user experience (e.g., by customizing content and services to the user's liking). The continuous miniaturization of electronics has led to it that many of the sensors used to measure affective response of users are wireless small devices—so small that they are often attached to users' bodies, and even in some cases, implanted in them.
Wide-spread adoption of computational platforms such as mobile devices has made it possible for a user to communicate with large-scale networks such as the internet, practically anytime and from anyplace they choose. These platforms also give users the freedom to utilize services from a plethora of remote computational systems such as cloud-based computing applications. Consequently, users are exposed to large amounts of digital content many times a day, and for long periods of time. Some examples of content users are likely to be exposed to include various forms of digital media (e.g., internet sites, television shows, movies, and/or interactive computer games), communications with other users (e.g., video conversations and instant messages), and/or communications with a computer (e.g., interaction with a user's virtual agent).
As affective computing applications become more popular, users may be having their affective response measured for long periods; consequently, copious amounts of data may be generated and may need to be transmitted by the sensors. This raises several issues concerning the energy resources and possible safety concerns involved in transmitting large amounts of data. For example, sensors like cameras or EEG sensors often produce many high dimensional data points. Processing this data before transmitting it, such as filtering, analyzing, extracting features, compressing, and/or encrypting can require a system to perform a significant amount of computations, which require a non-negligible expenditure of energy. In addition, transmitting the data, which often involves wireless transmission, can also require an expenditure of energy. Therefore, preparing and/or transmitting affective response measurements may require expenditure of energy from sensors, which often have at their disposal only a limited energy supply for their operation.
Having a sensor that is attached to a user's body and/or implanted in it transmit large amounts of data also raises safety concerns. Transmitting large amounts of data may require the sensor's transmitter to increase the power it draws and/or durations of its transmissions. And these factors may increase associated risks, especially when a transmitter is near vital organs such as the brain.
Therefore, unrestrained transmission of sensor measurement data may deplete the sensor's power supply and may also cause possible health risks. Thus, there is a need to reduce the amount of affective response measurement data transmitted by sensors that are attached to, and/or implanted in, a user's body. The reduction should be achieved without dramatically reducing the performance of the systems utilizing the affective response measurements.